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Higher-level application of adaptive dynamic programming/reinforcement learning - A next phase for controls and system identification?

Higher-level application of Adaptive Dynamic Programming/Rei...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Lendaris, George G. Systems Science Graduate Program Portland State University Portland OR United States

ISBN: (纸本)9781424498888

In previous work it was shown that adaptive-Critic-type Approximate dynamic programming could be applied in a higher-level way to create autonomous agents capable of using experience to discern context and select optimal, context-dependent control policies. Early experiments with this approach were based on full a priori knowledge of the system being monitored. The experiments reported in this paper, using small neural networks representing families of mappings, were designed to explore what happens when knowledge of the system is less precise. Results of these experiments show that agents trained with this approach perform well when subject to even large amounts of noise or when employing (slightly) imperfect models. The results also suggest that aspects of this method of context discernment are consistent with our intuition about human learning. The insights gained from these explorations can be used to guide further efforts for developing this approach into a general methodology for solving arbitrary identification and control problems. © 2011 ieee.

关键词： reinforcement learning

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adaptive dynamic programming with balanced weights seeking strategy

Adaptive dynamic programming with balanced weights seeking s...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Fu, Jian He, Haibo Ni, Zhen School of Automation Wuhan University of Technology Wuhan Hubei 430070 China Department of Electrical Computerand Biomedical Engineering University of Rhode Island Kingston RI 02881 United States

ISBN: (纸本)9781424498888

In this paper we propose to integrate the recursive Levenberg-Marquardt method into the adaptive dynamic programming (ADP) design for improved learning and adaptive control performance. Our key motivation is to consider a balanced weight updating strategy with the consideration of both robustness and convergence during the online learning process. Specifically, a modified recursive Levenberg-Marquardt (LM) method is integrated into both the action network and critic network of the ADP design, and a detailed learning algorithm is proposed to implement this approach. We test the performance of our approach based on the triple link inverted pendulum, a popular benchmark in the community, to demonstrate online learning and control strategy. Experimental results and comparative study under different noise conditions demonstrate the effectiveness of this approach. © 2011 ieee.

关键词： Inverted pendulum

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Supervised adaptive dynamic programming based adaptive cruise control

Supervised adaptive dynamic programming based adaptive cruis...

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symposium Series on Computational Intelligence, ieee SSCI2011 - 2011 ieee symposium on adaptive dynamic programming and reinforcement learning, ADPRL 2011

作者： Zhao, Dongbin Hu, Zhaohui Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424498888

This paper proposes a supervised adaptive dynamic programming (SADP) algorithm for the full range adaptive cruise control (ACC) system. The full range ACC system considers both the ACC situation in highway system and the stop and go (SG) situation in urban street way system. It can autonomously drive the host vehicle with desired speed and distance to the preceding vehicle in both situations. A traditional adaptive dynamic programming (ADP) algorithm is suited for this problem, but it suffers from the low learning efficiency. We propose the concept of inducing range to construct the supervisor and finally formulate the SADP algorithm, which greatly speeds up the learning efficiency. Several driving scenarios are designed and tested with the trained controller compared to traditional ones by simulation results, showing that trained SADP performs very well in all the scenarios, so that it provides an effective approach for the full range ACC problem. © 2011 ieee.

关键词： adaptive cruise control

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An approximate dynamic programming based controller for an underactuated 6DoF quadrotor

An approximate Dynamic Programming based controller for an u...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Stingu, Emanuel Lewis, Frank L. Automation and Robotics Research Institute University of Texas at Arlington Arlington TX United States

ISBN: (纸本)9781424498888

This paper discusses how the principles of adaptive dynamic programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning using an actor-critic structure. Due to the complexity of the quadrotor system, the learning process has to use as much information as possible about the system and the environment. Various methods to improve the learning speed and efficiency are presented. Neural networks with local activation functions are used as function approximators because the state-space can not be explored efficiently due to its size and the limited time available. The complex dynamics is controlled by a single critic and by multiple actors thus avoiding the curse of dimensionality. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function. © 2011 ieee.

关键词： reinforcement learning

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adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

Adaptive dynamic programming for optimal control of unknown ...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Liu, Derong Wang, Ding Zhao, Dongbin Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424498888

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach. © 2011 ieee.

关键词： Cost functions

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Enhancing the episodic natural actor-critic algorithm by a regularisation term to stabilize learning of control structures

Enhancing the episodic natural actor-critic algorithm by a r...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Witsch, Andreas Reichle, Roland Geihs, Kurt Lange, Sascha Riedmiller, Martin Distributed Systems Group Universität Kassel Germany Machine Learning Lab Albert-Ludwigs-Universität Freiburg Germany

ISBN: (纸本)9781424498888

Incomplete or imprecise models of control systems make it difficult to find an appropriate structure and parameter set for a corresponding control policy. These problems are addressed by reinforcement learning algorithms like policy gradient methods. We describe how to stabilise the policy gradient descent by introducing a regularisation term to enhance the episodic natural actor-critic approach. This allows a more policy independent usage. © 2011 ieee.

关键词： reinforcement learning

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A reinforcement learning approach for sequential mastery testing

A reinforcement learning approach for sequential mastery tes...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： El-Alfy, El-Sayed M. College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781424498888

This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing. © 2011 ieee.

关键词： reinforcement learning

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adaptive sample collection using active learning for kernel-based approximate policy iteration

Adaptive sample collection using active learning for kernel-...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Liu, Chunming Xu, Xin Haiyun Hu Dai, Bin College of Mechatronics and Automation National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781424498888

Approximate policy iteration (API) has been shown to be a class of reinforcement learning methods with stability and sample efficiency. However, sample collection is still an open problem which is critical to the performance of API methods. In this paper, a novel adaptive sample collection strategy using active learning-based exploration is proposed to enhance the performance of kernel-based API. In this strategy, an online kernel-based least squares policy iteration (KLSPI) method is adopted to construct nonlinear features and approximate the Q-function simultaneously. Therefore, more representative samples can be obtained for value function approximation. Simulation results on typical learning control problems illustrate that by using the proposed strategy, the performance of KLSPI can be improved remarkably. © 2011 ieee.

关键词： reinforcement learning

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Bayesian active learning with basis functions

Bayesian active learning with basis functions

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Ryzhov, Ilya O. Powell, Warren B. Operations Research and Financial Engineering Princeton University Princeton NJ 08544 United States

ISBN: (纸本)9781424498888

A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may lead to poor decisions, making it necessary to sometimes explore actions that appear to be suboptimal. We propose a Bayesian strategy for active learning with basis functions, based on the knowledge gradient concept from the optimal learning literature. The new method performs well in numerical experiments conducted on an energy storage problem. © 2011 ieee.

关键词： dynamic programming

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Feedback controller parameterizations for reinforcement learning

Feedback controller parameterizations for Reinforcement Lear...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Roberts, John W. Manchester, Ian R. Tedrake, Russ CSAIL MIT Cambridge MA 02139 United States

ISBN: (纸本)9781424498888

reinforcement learning offers a very general framework for learning controllers, but its effectiveness is closely tied to the controller parameterization used. Especially when learning feedback controllers for weakly stable systems, ineffective parameterizations can result in unstable controllers and poor performance both in terms of learning convergence and in the cost of the resulting policy. In this paper we explore four linear controller parameterizations in the context of REINFORCE, applying them to the control of a reaching task with a linearized flexible manipulator. We find that some natural but naive parameterizations perform very poorly, while the Youla Parameterization (a popular parameterization from the controls literature) offers a number of robustness and performance advantages. © 2011 ieee.

关键词： Parameterization

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